Analysis of harmonic current interaction in an industial plant

An analysis of current transients caused by the operation of a nearby device in an industrial plant is presented in the paper. The source of current transients in the factory lighting system was traced to the operation of the nearby six-pulse AC/DC converter. To determine the nature of the interaction, a measurement was done with a storage oscilloscope. Also, laboratory experiments on one lamp were conducted. It was possible to exclude the presence of a parallel resonance on this site. It was concluded that transients are caused by voltage notches in certain working regimes of the six-pulse converter. Possible solutions to the problem are separating the supplies of the converter and the lighting installation, filtering, or adding additional line reactance

Support Vector Machine in Prediction of Building Energy Demand Using Pseudo Dynamic Approach

Building's energy consumption prediction is a major concern in the recent years and many efforts have been achieved in order to improve the energy management of buildings. In particular, the prediction of energy consumption in building is essential for the energy operator to build an optimal operating strategy, which could be integrated to building's energy management system (BEMS). This paper proposes a prediction model for building energy consumption using support vector machine (SVM). Data-driven model, for instance, SVM is very sensitive to the selection of training data. Thus the relevant days data selection method based on Dynamic Time Warping is used to train SVM model. In addition, to encompass thermal inertia of building, pseudo dynamic model is applied since it takes into account information of transition of energy consumption effects and occupancy profile. Relevant days data selection and whole training data model is applied to the case studies of Ecole des Mines de Nantes, France Office building. The results showed that support vector machine based on relevant data selection method is able to predict the energy consumption of building with a high accuracy in compare to whole data training. In addition, relevant data selection method is computationally cheaper (around 8 minute training time) in contrast to whole data training (around 31 hour for weekend and 116 hour for working days) and reveals realistic control implementation for online system as well.Comment: Proceedings of ECOS 2015-The 28th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems , Jun 2015, Pau, Franc

Pseudo Dynamic Transitional Modeling of Building Heating Energy Demand Using Artificial Neural Network

International audienceThis paper presents the building heating demand prediction model with occupancy profile and operational heating power level characteristics in short time horizon (a couple of days) using artificial neural network. In addition, novel pseudo dynamic transitional model is introduced, which consider time dependent attributes of operational power level characteristics and its effect in the overall model performance is outlined. Pseudo dynamic model is applied to a case study of French Institution building and compared its results with static and other pseudo dynamic neural network models. The results show the coefficients of correlation in static and pseudo dynamic neural network model of 0.82 and 0.89 (with energy consumption error of 0.02%) during the learning phase, and 0.61 and 0.85 during the prediction phase respectively. Further, orthogonal array design is applied to the pseudo dynamic model to check the schedule of occupancy profile and operational heating power level characteristics. The results show the new schedule and provide the robust design for pseudo dynamic model. Due to prediction in short time horizon, it finds application for Energy Services Company (ESCOs) to manage the heating load for dynamic control of heat production system

Effect of distributed generation on protection of medium voltage cable grids

ABSTRAC

CONTROL OF MICRO-INVERTERS AS AN OVERVOLTAGE PREVENTION METHOD UNDER HIGH PV PENETRATION

Low voltage (LV) residential grids are generally not designed for high penetration of photovoltaic (PV) distributed generation. Maximization of PV output is not only opposed by solar energy intermittency, but also by grid impacts in form of reverse power flow and overvoltage. More intelligent control of PV inverters is required to balance the voltage requirements of the grid and maximum energy yield wanted by the end user. This paper discusses how micro-inverter topology could be utilized to handle overvoltage problem and avoid power output losses by applying an innovative control method. Control is realized as partial generation shedding at PV module level which is an optimized alternative comparing to conventional, entire PV array tripping in the event of overvoltage